Dissimilarity based ensemble of extreme learning machine for gene expression data classification

作者：Lu, Hui-juan^*; An, Chun-lin; Zheng, En-hui; Lu, Yi

来源：Neurocomputing, 2014, 128: 22-30.

DOI：10.1016/j.neucom.2013.02.052

• Extreme learning machine
• Dissimilarity ensemble
• Double-fault measure
• Majority voting
• Gene expression data

Extreme learning machine (ELM) has salient features such as fast learning speed and excellent generalization performance. However, a single extreme learning machine is unstable in data classification. To overcome this drawback, more and more researchers consider using ensemble of ELMs. This paper proposes a method integrating voting-based extreme learning machines (V-ELMs) with dissimilarity CD-ELM). First, based on different dissimilarity measures, we remove a number of ELMs from the ensemble pool. Then, the remaining ELMs are grouped as an ensemble classifier by majority voting. Finally we use disagreement measure and double-fault measure to validate the D-ELM. The theoretical analysis and experimental results on gene expression data demonstrate that (1) the D-ELM can achieve better classification accuracy with less number of ELMs; (2) the double-fault measure based D-ELM (DF-D-ELM) performs better than disagreement measure based D-ELM (D-D-ELM).

• 出版日期2014-3-27
• 单位中国矿业大学（北京）; 中国计量大学